Campus AI Research Engineer (Full-Time)
An ML engineering and research role at Jump that builds the training and inference systems behind its trading models. You sit between researchers, quants and engineers, working on GPU and HPC infrastructure.
What you’d work on
Our reading of the posting: what a new hire in this role would most likely spend their first months on.
- Build reusable frameworks for financial ML with researchers
- Speed up training pipelines on HPC and GPU clusters
- Put models into production where latency matters
- Make large ML systems observable and quick to iterate on
What they ask for
Required
- Python and/or C++
- PyTorch, JAX or TensorFlow
- GPU programming (CUDA, Triton, SYCL, ROCm)
- Large-scale ML systems experience
- Clear written and spoken English
- Education
- Not stated
- Experience
- Early-career campus hire; deep expertise expected in relevant areas
- Languages and tools
How to prepare
- Write a custom CUDA or Triton kernel and profile it
- Train a transformer across several GPUs and measure scaling
- Build a low-latency inference service and benchmark it
- Study data loading for very large datasets
What to study
- Financial Machine Learning
- Modern C++ for Quantitative Finance
- Trading Systems and Low-Latency Infrastructure
- Quantitative Data Engineering
The NLP and ML Quant Researcher roadmap sets out the stages for this kind of role, with free resources for each.
Open the roadmapApply
Read the full posting on Jump Trading’s site before applying: requirements, location and pay can change after we check them.
View the posting on Jump Trading’s site ↗
SelfMadeQuant wrote this summary from the employer’s public posting, last checked 6 November 2026. SelfMadeQuant isn’t affiliated with Jump Trading and doesn’t handle applications.